Age-related macular degeneration (AMD) is a major causes of visual disability and blindness. Although significant progress has been made in identifying genetic factors that underlie AMD, the majority of factors that determine disease risk remain unknown, leading to an interest in non-genetic factors that contribute to disease. Epigenetic alterations increase over time, in part as a result of environmental factors, and may explain the late onset of common diseases like AMD. Funded by our X01 grant, CIDR has determined the genome-wide DNA methylation patterns in peripheral blood for 500 individuals including AMD and primary open-angle glaucoma (POAG) patients, as well as age- and sex-matched controls. This grant, in response to the NEI's RFA on Integrative Data Analysis, will integrate various existing datasets including genetic, epigenetic, genomic annotation, and protein-protein interactions to discover novel risk factors associated with AMD. The necessary genome data exists and is publicly available, but tools to overlay and intersect both genetic and epigenetic data are lacking. The goal of this proposal is two-fold: (1) We will develop innovative bioinformatics approaches to interpret and integrate these diverse datasets; (2) We will identify novel genetic variations, epigenetic variations and the interactions between them that are associated with AMD. We propose to design, build, and use computational tools to execute integrated epigenetic and genetic analysis to pursue the following Aims. In Aim 1, we will integrate methylation and GWAS data in novel ways to identify novel genetic variations associations with AMD at the resolution of genomic sites. The disease-specific differentially methylated regions (DMRs) will be used as an intermediary phenotype, and genetic variations associated with the DMRs will be identified. By integrating the genetic datasets obtained from the AMD GWAS studies, we will identify methylation quantitative trait loci (methQTL) that not only can provide novel genetic variations associated with diseases but also can reveal epigenetic risk factors for AMD that are mediated genetically. In Aim 2 we will identify interactions between genetic and epigenetic variations associated with AMD at the resolution of genes. We will develop a network-based approach to identify interaction modules enriched for the genes that are either genetically altered or epigenetically altered to explore how these interactions might contribute to risk of the disease. The work described in this proposal is a critical step toward a better understanding of the underlying genetic and epigenetic risk factors that contribute to the development of AMD. Our study will also provide a general computational framework for integrative analyses for other human diseases. In the future, the identified DMRs could be experimentally validated and ultimately lead to new biomarkers and therapeutic targets for AMD.